Biden-Harris Administration Awards Nearly $80 Million For Wireless Innovation

The Department of Commerce’s National Telecommunications and Information Administration (NTIA) awarded nearly $80 million in the third round of grants from the Public Wireless Supply Chain Innovation Fund’s first Notice of Funding Opportunity. This round includes the first awards in the Testing & Evaluation (T&E) category, designed to expand industry-accepted T&E activities, making testing more accessible to the entire wireless ecosystem. The $1.5 billion Wireless Innovation Fund supports the development of open and interoperable wireless networks. Open and interoperable wireless equipment will help drive competition, strengthen global supply chain resilience and lower costs for consumers and network operators. The grantees for this round are:

• DISH Wireless will collaborate with Fujitsu Network Communications, Inc., Mavenir Systems, Inc., and VMWare, Inc. to create the Open RAN Center for Integration and Deployment
• VIAVI Solutions will establish a hybrid physical lab infrastructure and cloud-based testing lab-as-a-service (LaaS) or VALOR. VALOR aims to create a fully automated, cooperative, open and impartial testing-as-a-service (TaaS) offering that is dedicated to Open RAN interoperability, performance and security.
• Virginia Tech will develop a novel and comprehensive cybersecurity testing framework and demonstrate its effectiveness using 5G O-RAN Proof-of-Concept (PoC) testbeds.
• Cirrus360 will develop a new test method that uses a digital twin of integrated RAN components to model their implementation. 
• Northeastern University aims to develop and operationalize AutoRAN, a new testing methodology based on a collection of automated and continuous software pipelines designed to automatically test disaggregated Radio Access Network (RAN) components.
• Rice University will develop the ETHOS testing framework. This framework will examine communication performance along with the impact that the computing environment and machine learning domain have on RAN software performance.